qmd

Local Markdown search minimizes token usage during codebase exploration by locating relevant files and sections before you read them. It uses fast keyword and semantic similarity searches to retrieve only the necessary definitions or implementations.

Hybrid search for Markdown documentation combines fast BM25 keyword search with semantic similarity ranking to locate relevant notes. This approach refines results when simple keyword matching fails to find the correct sections across knowledge bases.

Yes, you can find function definitions in a large codebase without loading every file by running a local search query. This read-prioritization workflow retrieves only the specific sections needed, avoiding the token cost of opening multiple files.

Local Markdown search requires building a local index maintained through status checks, updates, and embedding commands. This indexing process enables the fast BM25 keyword search and semantic similarity retrieval needed to query documentation effectively.

The best way to locate notes in a knowledge repository when keywords fail is to use semantic similarity search. This method falls back from standard keyword search to match meaning rather than exact terms, refining the results for relevant sections.